1. Field of the Invention
The invention relates generally to the field of systems for model-based computation systems and artificial intelligence, and more specifically to systems for identifying and quantifying similarities and differences between and among data objects.
2. Background
One of the goals of computer science from very early in the field has been to provide an "intelligent" digital computing system, that is, to provide a digital computer which exhibits an artificial intelligence. One of the primary problems in artificial intelligence is analogical reasoning, which requires a determination of whether, and the degree to which, objects in a group of objects are similar. Each object in a group is essentially defined by a set of characteristics, and the group of objects is largely a database of the characteristics of the objects under scrutiny. The characteristics of the objects in the group may have been entered into the database by an operator. Alternatively, the computer itself may determine the characteristics of the objects under scrutiny if the computer has instruments or other means for determining the objects' characteristics; in this case, however, an operator normally will identify the relevant characteristics to the computer, as well has how to determine the characteristics.
Determining whether two objects in a group are identical is relatively simple. Essentially, all that need be done to determine if two objects are identical to one another is to perform a characteristic by characteristic comparison of the two objects. If all, or selected ones, of the characteristics match, the objects are identified as being identical, at least in regards the characteristics that are selected.
However, analogical reasoning cannot rely only on a determination that objects are identical. Normally, few objects in a group are identical to one another, and if the characteristics do not match, they will not be identified as being analogous. In addition, the similarity, or degree of analogy, between and among objects in a group also provides significant information about those objects which is useful in an analogical reasoning context. Accordingly, a substantial amount of effort has been expended to devise systems for performing analogical reasoning.
Initially, analogical reasoning was viewed as being essentially a form of inductive reasoning, primarily in connection with objects which were mathematical in nature. More recently, systems have been developed which are not limited to inductive reasoning. However, those systems require a high-level a priori knowledge of the objects being compared and the comparison to be performed.
In addition to analogy, which is related to a determination of the similarity among objects, symmetric comparison techniques have been devised which also take into account the differences between objects. In existing symmetric comparison techniques, the similarity and difference between two objects is represented as a function, specifically an inverse function, of the distance between the objects in a multidimensional space, with the number of dimensions in the space being the number of characteristics defining the objects. However, this approach becomes computationally unwieldy and difficult to interpret as the number of characteristics, and thus the number of dimensions, increases.
In addition, the multidimensional space approach to symmetric comparison assumes that the similarity judgements between objects is symmetric, since the distance in space is not dependent on the starting point for the measurement. However, in many instances, the similarity relationships between objects is not symmetric.